Assimilation of Cosmic‐Ray Neutron Counts for the Estimation of Soil Ice Content on the Eastern Tibetan Plateau

Abstract

Accurate observations and simulations of soil moisture phasal forms are crucial in cold region hydrological studies. In the seasonally frozen ground of eastern Tibetan Plateau, water vapor, liquid, and ice coexist in the frost‐susceptible silty‐loam soil during winter. Quantification of soil ice content is thus vital in the investigation and understanding of the region's freezing‐thawing processes. This study focuses on the retrieval of soil ice content utilizing the in situ soil moisture (i.e., liquid phase) and cosmic ray neutron measurements (i.e., total water including liquid and ice), with Observing System Simulation Experiments. To derive the total soil water from neutron counts, different weighting methods (revised, conventional, and uniform) for calibrating the cosmic‐ray neutron probe (CRNP) were intercompared. The comparison showed that the conventional nonlinear method performed the best. Furthermore, to assimilate fast neutrons using the particle filter, the STEMMUS‐FT (Simultaneous Transfer of Energy, Mass and Momentum in Unsaturated Soil) model was used as the physically based process model, and the COSMIC model (Cosmic‐ray Soil Moisture Interaction Code) used as the observation operator (i.e., forward neutron simulator). Other than background inputs from disturbed initializations in the STEMMUS‐FT, model uncertainties were predefined to assimilate fast neutrons. We observed that with enough spread of uncertainties, the updated states could mimic the CRNP observation. In all setups, assimilating CRNP measurements could enhance total soil water analyses, which consequently led to the improved detection of soil ice content and therefore the freezing thawing‐process at the field scale.

An Air-to-Soil Transition Model for Discrete Scattering-Emission Modelling at L-Band

“…If we can further take into account surface (soil) liquid water and ice content for the h estimation, the performance of the ATS model for L-band radiometry of the freeze-thawed soil is expected to be improved. It is to note that soil ice content cannot be measured directly in situ but can be retrieved indirectly via assimilating proximal sensing signals [73]. The inclusion of soil ice content, surface liquid water fraction, and ground surface temperature into the ATS model will be explored in further studies.…”

Multiyear in-situ L-band microwave radiometry of land surface processes on the Tibetan Plateau

“…As a further complication, when a land surface undergoes freeze-thaw processes, the behaviour of microwave observation abruptly changes in response to changes in the phase of the soil water (i.e., liquid or solid phase) at different soil depths. While such a dynamic process in space and time can be observed and modelled with in situ measurements 12 – 14 , current satellite retrievals can only provide freeze-thaw information (date and range of depths) using passive microwaves at a low resolution 15 , 16 or a binary indication of the frozen or thawed surfaces 17 . All these results point to a fundamental need to advance knowledge in understanding the precise scattering-emission mechanism of vegetated lands and the need for in-depth investigations of freeze-thaw processes.…”

“…This dataset can be used to validate satellite based observations and retrievals 31 , 32 , verify radiative transfer model assumptions 11 and validate land surface model and reanalysis outputs 14 , 30 , retrieve soil physical properties, as well as to quantify land-atmosphere exchanges of energy, water and carbon and help to reduce discrepancies and uncertainties in current Earth System Models (ESM) parameterizations.…”

An Integrative Information Aqueduct to Close the Gaps between Satellite Observation of Water Cycle and Local Sustainable Management of Water Resources

“…Therefore, there is a growing need to develop a downscaling procedure in order to reach a reference scale comparable with the emerging hyper-resolution modelling trend [35] and much finer resolution for water resources management. The spatial and temporal variability of the soil moisture process has been investigated by several authors that provided a clear path for the description of its dynamics [9,[36][37][38][39][40]. In this context, Qu et al (2015) [41] developed a method to predict the sub-grid variability of soil moisture based on basic soil data.…”